{"id":"https://openalex.org/W7164835388","doi":"https://doi.org/10.1145/3805622.3810866","title":"CausalGS: Learning Physical Causality of 3D Dynamic Scenes with Gaussian Representations","display_name":"CausalGS: Learning Physical Causality of 3D Dynamic Scenes with Gaussian Representations","publication_year":2026,"publication_date":"2026-06-15","ids":{"openalex":"https://openalex.org/W7164835388","doi":"https://doi.org/10.1145/3805622.3810866"},"language":null,"primary_location":{"id":"doi:10.1145/3805622.3810866","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805622.3810866","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3805622.3810866","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135965022","display_name":"Nengbo Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I5343935","display_name":"Guilin University of Electronic Technology","ror":"https://ror.org/05arjae42","country_code":"CN","type":"education","lineage":["https://openalex.org/I5343935"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nengbo Lu","raw_affiliation_strings":["Guilin University of Electronic Technology, Guilin, China"],"raw_orcid":"https://orcid.org/0009-0004-1791-0753","affiliations":[{"raw_affiliation_string":"Guilin University of Electronic Technology, Guilin, China","institution_ids":["https://openalex.org/I5343935"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100962972","display_name":"Minghua Pan","orcid":"https://orcid.org/0009-0004-2428-9280"},"institutions":[{"id":"https://openalex.org/I5343935","display_name":"Guilin University of Electronic Technology","ror":"https://ror.org/05arjae42","country_code":"CN","type":"education","lineage":["https://openalex.org/I5343935"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minghua Pan","raw_affiliation_strings":["Guilin University of Electronic Technology, Guilin, China"],"raw_orcid":"https://orcid.org/0009-0004-2428-9280","affiliations":[{"raw_affiliation_string":"Guilin University of Electronic Technology, Guilin, China","institution_ids":["https://openalex.org/I5343935"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.93718198,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"845","last_page":"854"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.4262000024318695,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.4262000024318695,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.32910001277923584,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.051600001752376556,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5875999927520752},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5389999747276306},{"id":"https://openalex.org/keywords/physical-system","display_name":"Physical system","score":0.5285000205039978},{"id":"https://openalex.org/keywords/physical-law","display_name":"Physical law","score":0.4756999909877777},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.4375},{"id":"https://openalex.org/keywords/differentiable-function","display_name":"Differentiable function","score":0.4108000099658966},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4099000096321106},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.3919000029563904},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.3628999888896942}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.59579998254776},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5875999927520752},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5609999895095825},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5389999747276306},{"id":"https://openalex.org/C116672817","wikidata":"https://www.wikidata.org/wiki/Q1454986","display_name":"Physical system","level":2,"score":0.5285000205039978},{"id":"https://openalex.org/C194583477","wikidata":"https://www.wikidata.org/wiki/Q408891","display_name":"Physical law","level":2,"score":0.4756999909877777},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.4375},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.4108000099658966},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4099000096321106},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3919000029563904},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3806999921798706},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3628999888896942},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.3628999888896942},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.36169999837875366},{"id":"https://openalex.org/C2780704645","wikidata":"https://www.wikidata.org/wiki/Q9251458","display_name":"Observer (physics)","level":2,"score":0.35519999265670776},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.34049999713897705},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C11302649","wikidata":"https://www.wikidata.org/wiki/Q6927157","display_name":"Moving frame","level":3,"score":0.3070000112056732},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.30649998784065247},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.27160000801086426},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.26170000433921814},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C51955184","wikidata":"https://www.wikidata.org/wiki/Q1545585","display_name":"Stochastic differential equation","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C2777021972","wikidata":"https://www.wikidata.org/wiki/Q22976830","display_name":"Uniqueness","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3805622.3810866","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805622.3810866","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3805622.3810866","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805622.3810866","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G381205167","display_name":null,"funder_award_id":"62361021","funder_id":"https://openalex.org/F4320336806","funder_display_name":"National Natural Science Foundation of China - State Grid Corporation Joint Fund for Smart Grid"}],"funders":[{"id":"https://openalex.org/F4320336806","display_name":"National Natural Science Foundation of China - State Grid Corporation Joint Fund for Smart Grid","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W3174752334","https://openalex.org/W3183092879","https://openalex.org/W4200150166","https://openalex.org/W4200502498","https://openalex.org/W4281651265","https://openalex.org/W4283770636","https://openalex.org/W4286611060","https://openalex.org/W4309436702","https://openalex.org/W4312815172","https://openalex.org/W4386066696","https://openalex.org/W4386075614","https://openalex.org/W4386075787","https://openalex.org/W4386076232","https://openalex.org/W4386076557","https://openalex.org/W4390872294","https://openalex.org/W4390872368","https://openalex.org/W4390873120","https://openalex.org/W4390889778","https://openalex.org/W4399411440","https://openalex.org/W4399574574","https://openalex.org/W4399989251","https://openalex.org/W4400573528","https://openalex.org/W4402713101","https://openalex.org/W4402716153","https://openalex.org/W4402727474","https://openalex.org/W4402727915","https://openalex.org/W4402753509","https://openalex.org/W4402753958","https://openalex.org/W4411425891","https://openalex.org/W4413145517","https://openalex.org/W4413146153","https://openalex.org/W4413146419","https://openalex.org/W4413146985","https://openalex.org/W4413147446","https://openalex.org/W4413557057","https://openalex.org/W4415797056","https://openalex.org/W7133184367","https://openalex.org/W7133221183","https://openalex.org/W7133232860","https://openalex.org/W7133239114","https://openalex.org/W7133240740","https://openalex.org/W7160095697"],"related_works":[],"abstract_inverted_index":{"Learning":[0],"a":[1,20,68,141,151],"physical":[2,10,135,201],"model":[3,191],"from":[4,81,107,214],"video":[5,109],"data":[6],"that":[7,70,101,157],"can":[8],"comprehend":[9],"laws":[11],"and":[12,125,203],"predict":[13],"the":[14,39,72,87,103,108,111,117,122,126,147,160,163,190,196,205,209,220],"future":[15,169],"trajectories":[16],"of":[17,41,75,114,167],"objects":[18],"is":[19,95,137,192],"formidable":[21],"challenge":[22],"in":[23,38,150,177],"artificial":[24],"intelligence.":[25],"Prior":[26],"approaches":[27],"either":[28],"leverage":[29],"various":[30],"Partial":[31],"Differential":[32],"Equations":[33],"(PDEs)":[34],"as":[35],"soft":[36],"constraints":[37],"form":[40],"PINN":[42],"losses,":[43],"or":[44,58],"integrate":[45],"physics":[46,98,143],"simulators":[47],"into":[48,110],"neural":[49],"networks;":[50],"however,":[51],"they":[52],"often":[53],"rely":[54],"on":[55,89,162],"strong":[56],"priors":[57],"high-quality":[59],"geometry":[60],"reconstruction.":[61],"In":[62],"this":[63],"paper,":[64],"we":[65],"propose":[66],"CausalGS,":[67],"framework":[69],"learns":[71],"causal":[73,206],"dynamics":[74,105],"complex":[76,104,197],"dynamic":[77,211],"3D":[78],"scenes":[79],"solely":[80,213],"multi-view":[82],"videos,":[83],"while":[84,172],"dispensing":[85],"with":[86],"reliance":[88],"explicit":[90],"priors.":[91],"At":[92],"its":[93,131],"core":[94],"an":[96],"inverse":[97],"inference":[99,113],"module":[100],"decouples":[102],"problem":[106],"joint":[112],"two":[115],"factors:":[116],"initial":[118],"velocity":[119],"field":[120],"representing":[121],"scene\u2019s":[123,210],"kinematics,":[124],"intrinsic":[127],"material":[128],"properties":[129,202],"governing":[130],"dynamics.":[132],"This":[133],"inferred":[134],"information":[136],"then":[138],"utilized":[139],"within":[140],"differentiable":[142],"simulator":[144],"to":[145,194],"guide":[146],"learning":[148],"process":[149],"physics-regularized":[152],"manner.":[153],"Extensive":[154],"experiments":[155],"demonstrate":[156],"CausalGS":[158],"surpasses":[159],"state-of-the-art":[161],"highly":[164],"challenging":[165],"task":[166],"long-term":[168],"frame":[170],"extrapolation,":[171],"also":[173],"exhibiting":[174],"advanced":[175],"performance":[176],"novel":[178],"view":[179],"interpolation.":[180],"Crucially,":[181],"our":[182],"work":[183],"shows":[184],"that,":[185],"without":[186],"any":[187],"human":[188],"annotation,":[189],"able":[193],"learn":[195],"interactions":[198],"between":[199],"multiple":[200],"understand":[204],"relationships":[207],"driving":[208],"evolution,":[212],"visual":[215],"observations.":[216],"We":[217],"anonymously":[218],"provide":[219],"code":[221],"at":[222],"https://github.com/DustSettled/CausalGS.":[223]},"counts_by_year":[],"updated_date":"2026-06-16T07:37:23.134862","created_date":"2026-06-16T00:00:00"}